Method for Combining Data Farming and Data Mining in a Logistics Assistance System for Materials Trading Networks Based on Graph Databases

To maintain the competitiveness of a materials trading network, decision-makers are
confronted with a multitude of logistics tasks. Finding answers to these tasks often
involves a decision-making process, which in turn requires a detailed analysis and
evaluation of the state of the materials trading network. Typically, logistics assistance
systems are used for this purpose, as they include various methods for this purpose,
such as simulation.
This dissertation develops a novel method for logistics assistance systems by combining
simulation-based data generation, called data farming, and knowledge discovery
in the domain of materials trading networks. By combining data farming and
knowledge discovery, logistics tasks can be addressed in a targeted manner and the
knowledge gained can be made available to the decision-makers of a materials trading
company. The method includes a modeling concept for developing a simulation
model using labeled property graphs, integrates data storage in graph databases, and
motivates the use of mining algorithms suitable for graph data.
The method is evaluated, and its applicability is demonstrated via a use case based on
observational data from a materials trading company. A critical re ection illustrates
the feasibility of the method, highlights advantages, and discusses limitations